Designing high-efficiency organic semi-conductors for organic photodetectors assisted by machine learning and property prediction

Abstract

Quantum chemical calculations have been widely utilized to design and explore organic semi-conductors for various photovoltaics applications. Photovoltaic devices such as photodetectors work on the principle of photo-electric-effect and widely used for optical communications. In recent years, machine learning has become famous in many research fields. In present study, we have predicted reorganization energy of organic semi-conductors with the aid of machine learning. Statistical analysis is used to select best molecular descriptors (features). About 10 machine learning models are tested for selecting the best one. Among various tested models, gradient forest regressor and random forest regressor are the top models. By using our new designing approach, easy to synthesize building blocks are connected to design new organic semiconductors. Their reorganization energies are predicted using fast machine learning method. For this purpose, already trained and hyper parameter optimized gradient boosting regressor is used. It is generally accepted that to obtain higher charge mobility molecules should have lower reorganization energy. Ten organic semi-conductors with lower reorganization energy are selected for density functional calculations and their energy levels are calculated. The chemical similarity analysis using clustering and heatmap is also done for selected organic semi-conductors. Synthetic accessibility score is calculated and majority of semi-conductors are easy to synthesize. These molecules are potential candidates for device fabrication. The proposed python-based pipeline has ability to select the molecules for organic photodetectors in fast and cheaper way.